MDH -9.51NTRIR3pp 0.0033NADPHQR4 0.00NADH17pp 0.00MALt3pp 0.00D_LACt2pp 0.00G1PPpp 0.00ACONMT 0.00ACALDtex 0.00GLYCTO3 0.00ACONIs 0.00CYTBD2pp 0.000.5PPC 22.0F6Ptex 0.00MALS 0.00HYD3pp 0.0022FBA3 9.98ETOHtrpp -5.19CYTBDpp 0.000.5FUMt2_2pp 0.00GLCP 0.00NO3R2bpp 0.00NADH18pp 0.00DSBAO1 0.00TMAOR2pp 0.00CS 0.0285GLGC 0.00GLYCtpp 0.00GLYCTO4 0.00PGI 10.0GLYCtex 0.00QMO3 0.0022GLYOX 0.00PGK -19.9FUMtex 0.00CAT 0.0022ETOHtex -5.19H2tpp 0.00SPODMpp 0.0022G3PD5 0.00PGMT 0.00G6PDH2r 0.00ACS 0.00GLYK 0.00AKGt2rpp 0.00MALt2_2pp 0.00PGL 0.0012PPDRtpp 0.00DMSOtpp 0.00ATPS4rpp -7.3043NADH16pp 0.00ACKr -14.4GLDBRAN2 0.00RPI -0.0195XYLt2pp 0.00G1Ptex 0.00MALtex 0.00CYTBO3_4pp 0.000.5DMSOR1pp 0.00FRUptspp 0.00PPK2r (nd)L_LACD2 0.00PYK 0.00QMO2 0.00L_LACt2rpp 0.00NADH10 0.00GLYCDx 0.00MALt2_3pp 0.00DHAPT 0.00CO2tex 9.22NO3R1bpp 0.00DSBAO2 0.00GLYC3Pabcpp 0.00PPCK 12.1L_LACtex 0.00PDH 0.00ME2 9.78ACALDtpp 0.00FRUtex 0.00XYLabcpp 0.00FRD3 0.00PPA2 0.00ACt2rpp -14.4HYD2pp 0.0022POX 0.00NADPHQR2 0.00ACONTb 0.0285CITtex 0.00RIBabcpp 0.00FUM 0.270EDA 0.00PFL 28.9PPA 0.002NO3R1pp 0.00GLCP2 0.00TMAOR1pp 0.00NADH5 0.00LDH_D 0.00LCARR 0.00PGM -19.9RPE -0.0206FRD2 0.00878NADPHQR3 0.00GLYC3Ptex 0.00PPS 0.00GLBRAN2 0.00G3PD7 0.00GLCptspp 10.0TMAOR2 0.00FDH5pp 0.00SPODM 0.0022SUCCt2_2pp 0.00SUCCt2_3pp 0.00FBP 0.00HYD1pp 0.0022TPI 9.9712PPDRtex 0.00ICL 0.00ICDHyr 0.0285D_LACtex 0.00MDH3 0.00FORtex -28.9ENO 19.9L_LACD3 0.00G3PT 0.00GLCDpp 0.00GLCt2pp 0.00RBK 0.00PTAr 14.4GAPD 19.9CITt3pp 0.00880SUCOAS 0.00H2tex 0.00ACONTa 0.0285PFK 0.00LALDO2x 0.00ACtex -14.4ATPM 3.15PFK_3 9.98MGSA 0.00NADH9 0.00F6Pt6_2pp 0.00NTRIR2x 0.00CITt7pp 0.00880F6PA 0.00MDH2 0.00GLYC3Pt6pp 0.00PYRt2rpp 0.00GLYCTO2 0.00TKT1 -0.00524THD2pp 0.0022SUCCt3pp 0.00PPKr (nd)ALCD2x -5.19SUCCtex -0.00880DMSOR2 0.00ALDD2x 0.00G3PD2 -0.00369XYLI1 0.00NTRIR4pp 0.0033CITL 0.00FORt2pp 0.00GLCtex_copy1 0.00PYRtex 0.00SUCDi 0.00FORtppi 28.9TALA -9.98TKT2 -0.0154XYLtex 0.00AKGDH 0.00AKGtex 0.00NO3R2pp 0.00LGTHL 0.00ALDD2y 0.00EDD 0.00FRUK 0.00FDH4pp 0.00MOX -0.0000664CO2tpp 9.22ME1 0.00XYLK 0.00G6Pt6_2pp 0.00NADTRHD 0.00G3PD6 0.00GND 0.00HEX1 0.00DMSOR1 0.00FUMt2_3pp 0.00TMAOR1 0.00FBA 0.00G6PP 0.00RIBtex 0.00FRUpts2pp 0.00ACALD -5.19HCO3E 0.0140FHL 0.00GLCS1 0.00G6Ptex 0.00OAADC 0.00DMSOR2pp 0.00LDH_D2 0.00LDH_D2 0.00L_LACD2 0.00GLCptspp 10.0L_LACD3 0.00mal__L_cnad_cnadh_ch_coaa_cq8h2_cno2_ph_ph2o_pq8_cnh4_p2dmmq8_ch_cnadph_c2dmmql8_cnadp_cnadh_ch_cmqn8_cnad_ch_pmql8_ch_ph_cmal__L_plac__D_ph_plac__D_ch_cg1p_ph2o_ppi_pglc__D_pacon_T_camet_cahcys_caconm_cacald_eacald_pglyclt_cglx_cacon_C_ch_co2_ch2o_ch_ppep_ch2o_cco2_ch_cpi_cf6p_ef6p_ph2o_caccoa_cglx_ch_ccoa_ch2_ch_ch_ps17bp_ce4p_cdhap_cetoh_petoh_ch_co2_ch2o_ch_ph_pfum_ph_cfum_cpi_cglycogen_cg1p_cno3_pno2_ph2o_ph_cnadh_ch_pnad_cdsbard_pdsbaox_ptmao_ph_ph2o_ptma_paccoa_ch2o_ccit_ch_ccoa_catp_ch_cppi_cadpglc_cglyc_cglyc_pglyclt_cglx_cg6p_cf6p_cglyc_eo2_ch_co2s_ch2o_clgt__S_ch_cgthrd_catp_c3pg_cadp_c13dpg_cfum_eh2o2_ch2o_co2_cetoh_eh2_ph2_ch_po2s_ph2o2_po2_pglyc3p_cdhap_cnadp_cnadph_c6pgl_ch_cac_catp_ccoa_camp_cppi_catp_ch_cglyc3p_cadp_ch_pakg_ph_cakg_ch_ph_ch2o_c6pgc_ch_c12ppd__R_p12ppd__R_cdmso_pdmso_ch_padp_cpi_ch_catp_ch2o_cnadh_ch_cnad_ch_patp_cadp_cactp_cbglycogen_cr5p_cru5p__D_ch_pxyl__D_pxyl__D_ch_cg1p_emal__L_eo2_ch_ch2o_ch_pdmso_ph2o_pdms_ppep_cfru_pf1p_cpyr_catp_cppi_cpppi_cadp_clac__L_cq8_cq8h2_cadp_ch_catp_co2_co2s_ch_ch_plac__L_ph_cnadh_ch_cnad_cnad_cdha_cnadh_ch_ch_ph_cpep_cpyr_cco2_eco2_pno3_ph2o_pno2_pdsbard_pdsbaox_ph2o_cglyc3p_patp_ch_cpi_cadp_catp_cco2_cadp_clac__L_enad_ccoa_cnadh_cco2_cnadp_cco2_cnadph_cacald_cfru_eh2o_catp_cadp_cpi_ch_c2dmmql8_csucc_c2dmmq8_ch2o_ch_cpi_ch_pac_ph_ch2_ch_ch_ppyr_ch2o_cco2_cac_cnadph_ch_cnadp_ch2o_cicit_ccit_ecit_prib__D_ph2o_catp_cpi_cadp_crib__D_ch_ch2o_c2ddg6p_cg3p_ccoa_cfor_ch2o_ch_cpi_cno3_ch_ch_pno2_ch2o_cpi_ch_ptmao_ptma_ph2o_pnadh_ch_cnad_cnad_ch_cnadh_ch_cnadh_clald__D_cnad_c2pg_cxu5p__D_cmql8_cmqn8_cnadph_ch_cnadp_cglyc3p_eatp_ch2o_camp_cpi_ch_cglyc3p_cdhap_cpep_cpyr_ctmao_ch_ch2o_ctma_cfor_ph_cco2_ph_po2s_ch_ch2o2_co2_csucc_ph_ph_ch_ph_cfdp_ch2o_cpi_ch2_ch_ch_p12ppd__R_enadp_cco2_cnadph_clac__D_emqn8_cmql8_cfor_efor_ph2o_ch2o_cpi_cglc__D_ph2o_ph_pglcn_ph_ph_cglc__D_catp_cadp_ch_cpi_ccoa_cpi_cnad_ch_cnadh_ch_ph_ccoa_catp_cpi_csuccoa_cadp_ch2_eatp_cadp_ch_cnadh_ch_cmthgxl_cnad_cac_eatp_ch2o_cadp_ch_cpi_catp_cs7p_cadp_ch_cpi_cnadh_ch_cnad_cpi_cpi_pnadh_ch_cno2_cnad_cnh4_ch2o_clac__L_cpyr_csucc_csucc_pq8_cq8h2_cpi_cpi_ph_ppyr_ph_cglyclt_cglx_ch_pnadh_cnadp_ch_cnad_cnadph_ch_ph_catp_cadp_cnad_ch_cnadh_csucc_eh2o_cdms_cnad_ch2o_cnadh_ch_cnadp_ch_cnadph_cxylu__D_ch_pno2_pnh4_ph2o_pac_ch_ph_cglc__D_epyr_eq8_cq8h2_cxyl__D_enad_ccoa_cnadh_cco2_cakg_eh_cno3_ch_pno2_ch2o_cnadp_ch2o_cnadph_ch_ch2o_catp_ch_cadp_ch_cfor_pco2_ph_po2_ch2o2_cco2_cnad_cnadh_cco2_catp_ch_cadp_cpi_cg6p_ppi_pglyc3p_cdhap_cnadp_cnadph_cco2_catp_cadp_ch_cdmso_cdms_ch2o_ch_ph_ctmao_ch_ch2o_ctma_ch2o_cpi_crib__D_epep_cpyr_cnad_ccoa_ch_cnadh_cco2_ch2o_ch_chco3_ch_cco2_ch_cadp_cg6p_eh_cco2_ch2o_pdms_ppyr_clac__D_cq8h2_cq8_cpyr_clac__L_cglc__D_pg6p_cmqn8_cpyr_cmql8_cNitrite ReductaseTranshydrogenaseCarbonateMenaquinone Reduction/OxidationATPSynthaseDemethylmenaquinone Reduction/OxidationUbiquinone Reduction/OxidationATP MaintenanceOxidative Stress
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Escher.ipynb

PIBIC
PDH
PFL
Fluxos melhor caso (PDH e MDH)
Fluxos melhor caso (PDH, MDH e AKGDH)
Exportaçao de malato
//IC After Dan/Escher/
Name
ModifiedLast Modified
  • e_coli_core.jsonlast mo.
  • Escher.ipynb1s ago
  • iJO1366.jsonlast mo.
Notebook
Python (cnapy)
Kernel status: Idle
    [1]:
    import cobra
    from cobra.io import load_json_model
    from cobra.flux_analysis import flux_variability_analysis

    import pandas as pd
    import numpy as np

    from cnapy.core import make_scenario_feasible
    openjdk version "21.0.8" 2025-07-15
    OpenJDK Runtime Environment (Red_Hat-21.0.8.0.9-1) (build 21.0.8+9)
    OpenJDK 64-Bit Server VM (Red_Hat-21.0.8.0.9-1) (build 21.0.8+9, mixed mode, sharing)
    
    Set parameter Username
    Set parameter LicenseID to value 2644080
    Academic license - for non-commercial use only - expires 2026-03-29
    
    [4]:
    R_imp = ['GLCptspp_fw','PDH_fw','PPC_fw','CS_fw','ACONTa_fw','ACONTa_bw',
    'ACONTb_fw','ACONTb_bw','ICDHyr_fw','ICDHyr_bw','AKGDH_fw','SUCOAS_fw','SUCOAS_bw',
    'FUM_fw','FUM_bw','MDH_fw','MDH_bw']
    [5]:
    def min_value(model,enzyme):
    bound = 0.0
    while True:
    with model:
    model.reactions.get_by_id(enzyme).bounds = (bound,bound)
    solution = model.optimize()

    if solution.objective_value is not None:
    bound -= 0.1

    else:
    return bound

    def max_value(model,enzyme):
    bound = 0.0
    while True:
    with model:
    model.reactions.get_by_id(enzyme).bounds = (bound,bound)
    solution = model.optimize()

    if solution.objective_value is not None:
    bound += 0.1

    else:
    return bound
    [7]:
    print(f'Max flux FUM_bw: {max_value(model,"FUM_bw")}')
    print(f'Min flux FUM_bw: {min_value(model,"FUM_bw")}')
    Max flux FUM_bw: 33.3000000000002
    Min flux FUM_bw: -0.1
    
    /home/cristian/.conda/envs/cnapy-1.2.4/lib/python3.10/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.
      warn(f"Solver status is '{status}'.", UserWarning)
    
    [8]:
    print(f'Max flux PDH_fw: {max_value(model,"PDH_fw")}')
    print(f'Min flux PDH_fw: {min_value(model,"PDH_fw")}')
    Max flux PDH_fw: 11.799999999999974
    Min flux PDH_fw: -19.1
    
    [9]:
    print(f'Max flux MDH_fw: {max_value(model,"MDH_fw")}')
    print(f'Min flux MDH_fw: {min_value(model,"MDH_fw")}')
    Max flux MDH_fw: 42.00000000000033
    Min flux MDH_fw: -35.10000000000023
    
    [10]:
    print(f'Max flux MDH_bw: {max_value(model,"MDH_bw")}')
    print(f'Min flux MDH_bw: {min_value(model,"MDH_bw")}')
    Max flux MDH_bw: 42.00000000000033
    Min flux MDH_bw: -2.700000000000001
    
    [11]:
    print(f'Max flux PPC_fw: {max_value(model,"PPC_fw")}')
    print(f'Min flux PPC_fw: {min_value(model,"PPC_fw")}')
    Max flux PPC_fw: 28.600000000000136
    Min flux PPC_fw: -0.1
    
    Max flux GLCptspp_fw: 0.0
    Min flux GLCptspp_fw: 0.0
    
    FUM_bw flux: 0.0
    PPC_fw flux: 0.41250750342026987
    GLCptspp_fw flux: 9.250127204153337
    PDH_fw flux: 0.0
    MDH_fw flux: 0.0
    MDH_bw flux: 0.0
    
    interactive(children=(FloatSlider(value=0.0, description='fum_bw', max=33.3, step=0.01), FloatSlider(value=0.4…
    [21]:
    Reaction identifierMDH_fw
    NameMalate dehydrogenase
    Memory address 0x7fc7441222c0
    Stoichiometry

    0.00236811413346649 enzyme_pool + mal__L_c + nad_c --> h_c + nadh_c + oaa_c

    0.00236811413346649 enzyme pool pseudometabolite + L-Malate + Nicotinamide adenine dinucleotide --> H+ + Nicotinamide adenine dinucleotide - reduced + Oxaloacetate

    GPRb3236
    Lower bound0.0
    Upper bound1000.0
    [50]:
    Reaction identifierFUM_bw
    NameFumarase
    Memory address 0x7f70707630a0
    Stoichiometry

    0.00694546169257057 enzyme_pool + mal__L_c --> fum_c + h2o_c

    0.00694546169257057 enzyme pool pseudometabolite + L-Malate --> Fumarate + H2O H2O

    GPRb2929 or b1675 or b1612 or b4122 or b1611
    Lower bound0.0
    Upper bound1000.0
    interactive(children=(FloatSlider(value=0.0, description='pdh', max=53.7, step=0.01), FloatSlider(value=0.0, d…
      [1]:
      import pandas as pd
      import matplotlib.pyplot as plt
      import numpy as np

      import cobra
      from cobra.io import load_json_model
      [2]:
      model = load_json_model('iCH360_h2.json')
      Set parameter Username
      Set parameter LicenseID to value 2644080
      Academic license - for non-commercial use only - expires 2026-03-29
      
      [ ]:

      [12]:
      resultados = {'NADH':[],'H2':[],'Biomassa':[]}

      for i in np.arange(0.0, 12, 0.1):
      try:
      with model:
      model.reactions.get_by_id('PDH_fw').bounds = (i,i)
      model.reactions.get_by_id('ACALD_fw').bounds = (0,0)
      model.reactions.get_by_id('LDH_fw').bounds = (0,0)
      solution = model.optimize()

      if solution.objective_value is not None:
      prod_nadh = sum(model.metabolites.get_by_id('nadh_c').summary(solution).producing_flux['flux'])
      prod_h2 = sum(model.metabolites.get_by_id('h2_c').summary(solution).producing_flux['flux'])
      resultados['NADH'].append(prod_nadh)
      resultados['H2'].append(prod_h2)
      resultados['Biomassa'].append(solution.objective_value)
      except:
      continue
      [13]:
      resultados
      [13]:
      {'NADH': [], 'H2': [], 'Biomassa': []}
      [10]:
      NADH H2 Biomassa
      0 11.620166 22.951292 0.118331
      1 11.624185 22.959178 0.118392
      2 11.628203 22.967063 0.118454
      3 11.632221 22.974948 0.118516
      4 11.636239 22.982834 0.118578
      ... ... ... ...
      115 29.223417 23.220535 0.036321
      116 29.458054 23.413378 0.035142
      117 29.692691 23.606222 0.033964
      118 29.927328 23.799065 0.032785
      119 30.161965 23.991909 0.031606

      120 rows × 3 columns

      Read LP format model from file /tmp/tmp2dm6hnxe.lp
      Reading time = 0.00 seconds
      : 310 rows, 1022 columns, 4870 nonzeros
      
      [15]:
      Optimal solution with objective value 0.077
      fluxes reduced_costs
      NDPK5_fw 0.002079 -2.927346e-18
      SHK3Dr_fw 0.029247 -1.287490e-18
      NDPK6_fw 0.002014 -9.714451e-17
      NDPK8_fw 0.002014 -1.214306e-17
      DHORTS_fw 0.000000 -9.303853e-03
      ... ... ...
      HYDFDN_bw 0.000000 -3.580570e-03
      PFOR_fw 4.223553 -7.589415e-19
      EX_h2_e_fw 16.894211 0.000000e+00
      H2TPP_fw 16.894211 0.000000e+00
      H2tex_fw 16.894211 0.000000e+00

      511 rows × 2 columns

      [28]:
      Biomassa NADH
      SA 0.617 28.3100
      SAn 0.103 18.5300
      M 26.070 0.0102
        [1]:
        import cobra
        from cobra.io import load_json_model, save_json_model
        [2]:
        model = load_json_model('Escherichia_coli_iCH360.json')
        Set parameter Username
        Set parameter LicenseID to value 2644080
        Academic license - for non-commercial use only - expires 2026-03-29
        
        [3]:
        model.reactions.get_by_id('EX_o2_e').bounds = (0,0)
        [4]:
        model.add_metabolites([cobra.Metabolite('fdxrd_c',compartment='c'),
        cobra.Metabolite('fdxox_c',compartment='c'),
        cobra.Metabolite('h2_c',compartment='c'),
        cobra.Metabolite('h2_e',compartment='e'),
        cobra.Metabolite('h2_p',compartment='p')
        ])

        HYDFDN = cobra.Reaction('HYDFDN')
        EX_h2_e = cobra.Reaction('EX_h2_e')
        PFOR = cobra.Reaction('PFOR')
        H2TPP = cobra.Reaction('H2TPP')
        H2tex = cobra.Reaction('H2tex')

        model.add_reactions([HYDFDN,
        PFOR,
        EX_h2_e,
        H2TPP,
        H2tex])

        # Define reaction equations
        HYDFDN.reaction = '3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c <--> 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c'
        PFOR.reaction = '1.0 coa_c + 1.0 pyr_c + 2.0 fdxox_c -> 1.0 accoa_c + 1.0 co2_c + 1.0 h_c + 2.0 fdxrd_c'

        H2TPP.reaction = '1 h2_c -> 1 h2_p' #cytosol to periplasm
        H2tex.reaction='1 h2_p -> 1 h2_e' #periplasm to external
        EX_h2_e.reaction = '1 h2_e ->'
        ### EC-iCH360

        Para modificar o EC-iCH360, o ideal seria colocar o enzyme pool para cada reação. Para isso, a reação da hydrogenase precisa ser separada em duas:

        HYDFDN_fw: 3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c + enzyme_pool_fw-> 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c
        HYDFDN_bw: 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c + enzyme_pool_bw-> 3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c

        Calculo dos enzyme_pools:
        - Massa molecular da HydABC (https://doi.org/10.1021/jacs.2c11683) = (306+348)/2 = 327 kDa
        - turnover produção de H2 (https://doi.org/10.1016/S0005-2728(99)00062-6) = (24+40)/2 = 32 s-1
        - turnover produção de H2 (https://doi.org/10.1016/S0005-2728(99)00062-6) = (120+190)/2 = 155 s-1

        A formula para calular o enzyme pool é: e = M/(turnover*saturation)

        Esse saturation irei adotar como 1 para os dois enzymes pool. Também temos que converter os turnovers, uma vez que estão em s-1. Para passar para h-1, multiplicamos por 3600, ficando com:

        EC-iCH360¶

        Para modificar o EC-iCH360, o ideal seria colocar o enzyme pool para cada reação. Para isso, a reação da hydrogenase precisa ser separada em duas:

        HYDFDN_fw: 3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c + enzyme_pool_fw-> 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c HYDFDN_bw: 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c + enzyme_pool_bw-> 3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c

        Calculo dos enzyme_pools:

        • Massa molecular da HydABC (https://doi.org/10.1021/jacs.2c11683) = (306+348)/2 = 327 kDa
        • turnover produção de H2 (https://doi.org/10.1016/S0005-2728(99)00062-6) = (24+40)/2 = 32 s-1
        • turnover produção de H2 (https://doi.org/10.1016/S0005-2728(99)00062-6) = (120+190)/2 = 155 s-1

        A formula para calular o enzyme pool é: e = M/(turnover*saturation)

        Esse saturation irei adotar como 1 para os dois enzymes pool. Também temos que converter os turnovers, uma vez que estão em s-1. Para passar para h-1, multiplicamos por 3600, ficando com:

        • turnover produção H2 = 32*3600 = 115200
        • turnover consumo H2 = 155*3600 = 558000

        Com isso, temos:

        • enzyme_pool_fw = 327/115200 = 0.002838542
        • enzyme_pool_bw = 327/558000 = 0.000586022

        Observe que a produção de H2 é mais penalizada que o consumo.

        Temos que fazer o mesmo para a PFOR:

        • turnover (https://www.brenda-enzymes.org/enzyme.php?ecno=1.2.7.1#TURNOVER%20NUMBER%20[1/s]) = 60.5*3600 = 217800 h-1
        • Massa molecular (BRENDA): 67 kDa
        • enzyme_pool = 67/217800 = 0.000308278
        Set parameter Username
        Set parameter LicenseID to value 2644080
        Academic license - for non-commercial use only - expires 2026-03-29
        
        [4]:
        np.float64(0.05991273287416082)
        [7]:
        Optimal solution with objective value 0.120
        fluxes reduced_costs
        NDPK5_fw 0.003234 4.662069e-18
        SHK3Dr_fw 0.045490 0.000000e+00
        NDPK6_fw 0.003132 0.000000e+00
        NDPK8_fw 0.003132 0.000000e+00
        DHORTS_fw 0.000000 -8.574123e-03
        ... ... ...
        HYDFDN_bw 0.000000 -3.299734e-03
        PFOR_fw 5.781830 -2.168404e-19
        EX_h2_e_fw 23.127319 0.000000e+00
        H2TPP_fw 23.127319 0.000000e+00
        H2tex_fw 23.127319 0.000000e+00

        511 rows × 2 columns

          [1]:
          import pandas as pd
          #import matplotlib.pyplot as plt
          import cobra
          import numpy as np
          from cobra.io import load_json_model
          from cobra.flux_analysis import flux_variability_analysis
          [2]:
          # Carregamento do modelo
          model = load_json_model('EC_iCH360.json')
          Set parameter Username
          Set parameter LicenseID to value 2644080
          Academic license - for non-commercial use only - expires 2026-03-29
          
          [9]:
          import re

          for i in model.reactions:
          i = str(i)
          if re.search("(^GLCptspp|^PDH|^PPC|^CS|^ACONT|^ICDHyr|^AKGDH|^SUCOAS|^SUCDi|^FUM|^MDH)", i):
          print(i)
          CS_fw: accoa_c + 0.00201858684593728 enzyme_pool + h2o_c + oaa_c --> cit_c + coa_c + h_c
          ICDHyr_fw: 0.00314748314882975 enzyme_pool + icit_c + nadp_c --> akg_c + co2_c + nadph_c
          PPCK_fw: atp_c + 0.0010183357374514 enzyme_pool + oaa_c --> adp_c + co2_c + pep_c
          MDH_fw: 0.00236811413346649 enzyme_pool + mal__L_c + nad_c --> h_c + nadh_c + oaa_c
          FUM_fw: 0.000286427523176266 enzyme_pool + fum_c + h2o_c --> mal__L_c
          PPC_fw: co2_c + 0.00140809547215078 enzyme_pool + h2o_c + pep_c --> h_c + oaa_c + pi_c
          AKGDH_fw: akg_c + coa_c + 0.00293247443496605 enzyme_pool + nad_c --> co2_c + nadh_c + succoa_c
          FUMt2_2pp_fw: 5.51101888723051e-05 enzyme_pool + fum_p + 2.0 h_p --> fum_c + 2.0 h_c
          FUMtex_fw: 0.000301543351233866 enzyme_pool + fum_e --> fum_p
          GLCptspp_fw: 0.000327143489166588 enzyme_pool + glc__D_p + pep_c --> g6p_c + pyr_c
          PDH_fw: coa_c + 0.00128810216333717 enzyme_pool + nad_c + pyr_c --> accoa_c + co2_c + nadh_c
          SUCOAS_fw: atp_c + coa_c + 0.0043694102601719 enzyme_pool + succ_c --> adp_c + pi_c + succoa_c
          ACONTb_fw: acon_C_c + 0.00151958365772561 enzyme_pool + h2o_c --> icit_c
          SUCDi_fw: 0.000703990974907043 enzyme_pool + q8_c + succ_c --> fum_c + q8h2_c
          ACONTa_fw: cit_c + 0.000956384163761154 enzyme_pool --> acon_C_c + h2o_c
          ICDHyr_bw: akg_c + co2_c + 0.00629504980277568 enzyme_pool + nadph_c --> icit_c + nadp_c
          MDH_bw: 0.0033676597842785 enzyme_pool + h_c + nadh_c + oaa_c --> mal__L_c + nad_c
          FUM_bw: 0.00694546169257057 enzyme_pool + mal__L_c --> fum_c + h2o_c
          FUMtex_bw: 0.000320175727991231 enzyme_pool + fum_p --> fum_e
          SUCOAS_bw: adp_c + 0.00111566402738508 enzyme_pool + pi_c + succoa_c --> atp_c + coa_c + succ_c
          ACONTb_bw: 0.00341252350407859 enzyme_pool + icit_c --> acon_C_c + h2o_c
          ACONTa_bw: acon_C_c + 0.00600259036351675 enzyme_pool + h2o_c --> cit_c
          
          [3]:
          R_imp = ['GLCptspp_fw','PDH_fw','PPC_fw','CS_fw','ACONTa_fw','ACONTa_bw',
          'ACONTb_fw','ACONTb_bw','ICDHyr_fw','ICDHyr_bw','AKGDH_fw','SUCOAS_fw','SUCOAS_bw',
          'SUCDi_fw','FUM_fw','FUM_bw','MDH_fw','MDH_bw']
          [4]:
          loop_reactions = [model.reactions.get_by_id(i) for i in R_imp]
          df_fva_aero = flux_variability_analysis(model, reaction_list=loop_reactions,
          loopless=True,fraction_of_optimum=0.9)
          [5]:
          df_fva_aero
          [5]:
          minimum maximum
          GLCptspp_fw 6.514073 13.406697
          PDH_fw 0.938670 12.450549
          PPC_fw 0.000000 9.549213
          CS_fw 0.594377 5.833653
          ACONTa_fw 0.594377 6.834471
          ACONTa_bw 0.000000 3.889498
          ACONTb_fw 0.594377 7.760801
          ACONTb_bw 0.000000 5.487901
          ICDHyr_fw 0.594377 6.242847
          ICDHyr_bw 0.000000 2.866489
          AKGDH_fw 0.000000 4.843314
          SUCOAS_fw 0.000000 5.089382
          SUCOAS_bw 0.000000 6.553354
          SUCDi_fw 0.000000 5.967491
          FUM_fw 0.000000 6.733280
          FUM_bw 0.000000 3.742717
          MDH_fw 0.000000 7.732268
          MDH_bw 0.000000 4.718965
          # Anaero

          Anaero¶

          Read LP format model from file /tmp/tmp34q97alk.lp
          Reading time = 0.00 seconds
          : 305 rows, 1010 columns, 4816 nonzeros
          
          [9]:
          minimum maximum
          GLCptspp_fw 8.480702 11.157505
          PDH_fw 0.000000 1.563670
          PPC_fw 0.305451 3.604094
          CS_fw 0.099471 0.490815
          ACONTa_fw 0.099471 3.557768
          ACONTa_bw 0.000000 3.458297
          ACONTb_fw 0.099471 4.978967
          ACONTb_bw 0.000000 4.879497
          ICDHyr_fw 0.099471 2.648173
          ICDHyr_bw 0.000000 2.548702
          AKGDH_fw 0.000000 0.276540
          SUCOAS_fw 0.000000 4.436308
          SUCOAS_bw 0.000000 4.387580
          SUCDi_fw 0.000000 0.000000
          FUM_fw 0.000000 3.334754
          FUM_bw 0.000000 3.328234
          MDH_fw 0.000000 4.195807
          MDH_bw 0.000000 4.195807
          [10]:
          minimum maximum
          SUCDi_fw 0.0 0.0
          [11]:
          minimum maximum
          GLCptspp_fw 8.480702 11.157505
          PDH_fw 0.000000 1.563670
          PPC_fw 0.305451 3.604094
          CS_fw 0.099471 0.490815
          ACONTa_fw 0.099471 3.557768
          ACONTa_bw 0.000000 3.458297
          ACONTb_fw 0.099471 4.978967
          ACONTb_bw 0.000000 4.879497
          ICDHyr_fw 0.099471 2.648173
          ICDHyr_bw 0.000000 2.548702
          AKGDH_fw 0.000000 0.276540
          SUCOAS_fw 0.000000 4.436308
          SUCOAS_bw 0.000000 4.387580
          FUM_fw 0.000000 3.334754
          FUM_bw 0.000000 3.328234
          MDH_fw 0.000000 4.195807
          MDH_bw 0.000000 4.195807
          [2]:
          minimum maximum
          GLCptspp 1.260000 10.000000
          PDH 0.000000 8.740000
          PPC 0.544038 4.515155
          CS 0.233694 2.418694
          ACONTa 0.233694 1.028240
          ACONTb 0.233694 1.028240
          ICDHyr 0.233694 1.028240
          AKGDH 0.000000 0.728333
          SUCOAS 0.000000 1.206579
          FUM -2.758714 4.524619
          MDH -3.644630 4.525163
          [12]:
          {'GLCptspp_fw': [8.480702017782255,
            8.778124559286447,
            9.075547100790638,
            9.37296964229483,
            9.670392183799024,
            9.967814725303215,
            10.265237266807407,
            10.562659808311599,
            10.86008234981579,
            11.157504891319983],
           'PDH_fw': [0.0,
            0.17374116081433766,
            0.3474823216286753,
            0.521223482443013,
            0.6949646432573506,
            0.8687058040716883,
            1.042446964886026,
            1.2161881257003637,
            1.3899292865147013,
            1.563670447329039],
           'PPC_fw': [0.30545066403546095,
            0.6719665350904158,
            1.0384824061453708,
            1.4049982772003253,
            1.7715141482552803,
            2.1380300193102353,
            2.50454589036519,
            2.871061761420145,
            3.2375776324750998,
            3.6040935035300548],
           'CS_fw': [0.09947094082781305,
            0.14295357129864042,
            0.1864362017694678,
            0.2299188322402952,
            0.27340146271112253,
            0.31688409318194993,
            0.36036672365277733,
            0.4038493541236047,
            0.4473319845944321,
            0.4908146150652595],
           'ACONTa_fw': [0.09947094082781305,
            0.4837261481877941,
            0.8679813555477751,
            1.2522365629077563,
            1.636491770267737,
            2.0207469776277183,
            2.4050021849876995,
            2.78925739234768,
            3.1735125997076614,
            3.5577678070676426],
           'ACONTa_bw': [0.0,
            0.38425520735998103,
            0.7685104147199621,
            1.1527656220799432,
            1.5370208294399241,
            1.921276036799905,
            2.3055312441598863,
            2.689786451519867,
            3.0740416588798483,
            3.4582968662398295],
           'ACONTb_fw': [0.09947094082781305,
            0.6416372223515296,
            1.1838035038752461,
            1.7259697853989628,
            2.2681360669226796,
            2.8103023484463963,
            3.3524686299701125,
            3.8946349114938292,
            4.436801193017546,
            4.978967474541262],
           'ACONTb_bw': [0.0,
            0.5421662815237166,
            1.0843325630474332,
            1.6264988445711497,
            2.1686651260948664,
            2.710831407618583,
            3.2529976891422994,
            3.795163970666016,
            4.337330252189733,
            4.879496533713449],
           'ICDHyr_fw': [0.09947094082781305,
            0.3826600187687306,
            0.6658490967096482,
            0.9490381746505657,
            1.2322272525914832,
            1.515416330532401,
            1.7986054084733185,
            2.081794486414236,
            2.3649835643551538,
            2.648172642296071],
           'ICDHyr_bw': [0.0,
            0.2831890779409176,
            0.5663781558818352,
            0.8495672338227527,
            1.1327563117636703,
            1.415945389704588,
            1.6991344676455054,
            1.982323545586423,
            2.2655126235273406,
            2.548701701468258],
           'AKGDH_fw': [0.0,
            0.030726721805839893,
            0.06145344361167979,
            0.09218016541751968,
            0.12290688722335957,
            0.15363360902919948,
            0.18436033083503936,
            0.21508705264087924,
            0.24581377444671915,
            0.27654049625255905],
           'SUCOAS_fw': [0.0,
            0.4929231074666134,
            0.9858462149332268,
            1.4787693223998402,
            1.9716924298664535,
            2.464615537333067,
            2.9575386447996803,
            3.4504617522662935,
            3.943384859732907,
            4.436307967199521],
           'SUCOAS_bw': [0.0,
            0.4875088394077107,
            0.9750176788154215,
            1.4625265182231322,
            1.950035357630843,
            2.4375441970385534,
            2.9250530364462644,
            3.4125618758539753,
            3.900070715261686,
            4.387579554669396],
           'FUM_fw': [0.0,
            0.3705282643807965,
            0.741056528761593,
            1.1115847931423894,
            1.482113057523186,
            1.8526413219039826,
            2.223169586284779,
            2.5936978506655755,
            2.964226115046372,
            3.3347543794271686],
           'FUM_bw': [0.0,
            0.3698037338214186,
            0.7396074676428372,
            1.1094112014642559,
            1.4792149352856745,
            1.8490186691070931,
            2.2188224029285117,
            2.58862613674993,
            2.958429870571349,
            3.3282336043927674],
           'MDH_fw': [0.0,
            0.4662007670349644,
            0.9324015340699288,
            1.3986023011048931,
            1.8648030681398575,
            2.3310038351748217,
            2.7972046022097863,
            3.263405369244751,
            3.729606136279715,
            4.195806903314679],
           'MDH_bw': [0.0,
            0.4662007670349644,
            0.9324015340699288,
            1.3986023011048931,
            1.8648030681398575,
            2.3310038351748217,
            2.7972046022097863,
            3.263405369244751,
            3.729606136279715,
            4.195806903314679]}
          [13]:
          1 2 3 4 5 6 7 8 9 10
          GLCptspp_fw 8.480702 8.778125 9.075547 9.372970 9.670392 9.967815 10.265237 10.562660 10.860082 11.157505
          PDH_fw 0.000000 0.173741 0.347482 0.521223 0.694965 0.868706 1.042447 1.216188 1.389929 1.563670
          PPC_fw 0.305451 0.671967 1.038482 1.404998 1.771514 2.138030 2.504546 2.871062 3.237578 3.604094
          CS_fw 0.099471 0.142954 0.186436 0.229919 0.273401 0.316884 0.360367 0.403849 0.447332 0.490815
          ACONTa_fw 0.099471 0.483726 0.867981 1.252237 1.636492 2.020747 2.405002 2.789257 3.173513 3.557768
          ACONTa_bw 0.000000 0.384255 0.768510 1.152766 1.537021 1.921276 2.305531 2.689786 3.074042 3.458297
          ACONTb_fw 0.099471 0.641637 1.183804 1.725970 2.268136 2.810302 3.352469 3.894635 4.436801 4.978967
          ACONTb_bw 0.000000 0.542166 1.084333 1.626499 2.168665 2.710831 3.252998 3.795164 4.337330 4.879497
          ICDHyr_fw 0.099471 0.382660 0.665849 0.949038 1.232227 1.515416 1.798605 2.081794 2.364984 2.648173
          ICDHyr_bw 0.000000 0.283189 0.566378 0.849567 1.132756 1.415945 1.699134 1.982324 2.265513 2.548702
          AKGDH_fw 0.000000 0.030727 0.061453 0.092180 0.122907 0.153634 0.184360 0.215087 0.245814 0.276540
          SUCOAS_fw 0.000000 0.492923 0.985846 1.478769 1.971692 2.464616 2.957539 3.450462 3.943385 4.436308
          SUCOAS_bw 0.000000 0.487509 0.975018 1.462527 1.950035 2.437544 2.925053 3.412562 3.900071 4.387580
          FUM_fw 0.000000 0.370528 0.741057 1.111585 1.482113 1.852641 2.223170 2.593698 2.964226 3.334754
          FUM_bw 0.000000 0.369804 0.739607 1.109411 1.479215 1.849019 2.218822 2.588626 2.958430 3.328234
          MDH_fw 0.000000 0.466201 0.932402 1.398602 1.864803 2.331004 2.797205 3.263405 3.729606 4.195807
          MDH_bw 0.000000 0.466201 0.932402 1.398602 1.864803 2.331004 2.797205 3.263405 3.729606 4.195807
          [20]:
          GLCptspp_fw PDH_fw PPC_fw CS_fw ACONTa_fw ACONTa_bw ACONTb_fw ACONTb_bw ICDHyr_fw ICDHyr_bw AKGDH_fw SUCOAS_fw SUCOAS_bw FUM_fw FUM_bw MDH_fw MDH_bw
          0 10.562660 1.216188 1.404998 0.099471 3.557768 3.074042 3.352469 0.542166 1.232227 1.982324 0.092180 1.478769 2.925053 0.741057 3.328234 3.263405 0.000000
          1 9.670392 0.173741 2.871062 0.360367 0.867981 1.537021 1.725970 0.000000 0.949038 0.000000 0.092180 0.492923 2.925053 1.852641 1.849019 2.331004 4.195807
          2 9.967815 1.389929 3.604094 0.316884 3.557768 2.305531 1.725970 0.542166 2.081794 0.566378 0.153634 2.464616 2.437544 1.852641 1.109411 0.932402 0.932402
          3 11.157505 0.173741 2.504546 0.360367 3.173513 1.537021 4.978967 2.168665 1.798605 2.548702 0.030727 1.971692 0.000000 0.370528 3.328234 2.331004 0.932402
          4 10.860082 0.694965 3.604094 0.447332 0.483726 0.768510 2.810302 0.542166 0.665849 1.699134 0.030727 0.985846 0.000000 2.593698 2.218822 2.331004 4.195807
          ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
          999995 10.860082 0.000000 0.671967 0.490815 3.173513 0.000000 3.352469 4.337330 0.099471 0.849567 0.122907 1.478769 0.487509 0.370528 0.369804 1.864803 4.195807
          999996 8.480702 0.694965 2.138030 0.229919 3.557768 1.921276 1.725970 4.337330 1.798605 2.548702 0.215087 2.957539 2.437544 1.852641 3.328234 1.398602 0.466201
          999997 8.778125 0.521223 3.237578 0.316884 3.557768 1.152766 1.725970 4.337330 1.232227 0.566378 0.000000 1.478769 2.437544 3.334754 1.479215 2.331004 0.000000
          999998 9.967815 0.521223 1.771514 0.403849 1.636492 2.305531 3.352469 1.084333 1.232227 2.548702 0.122907 2.957539 1.462527 2.593698 1.479215 0.932402 3.263405
          999999 10.562660 1.216188 0.671967 0.273401 2.405002 1.921276 3.352469 3.795164 2.364984 0.283189 0.184360 3.450462 2.925053 0.000000 2.218822 3.729606 1.864803

          1000000 rows × 17 columns

          [23]:
          GLCptspp_fw PDH_fw PPC_fw CS_fw ACONTa_fw ACONTa_bw ACONTb_fw ACONTb_bw ICDHyr_fw ICDHyr_bw AKGDH_fw SUCOAS_fw SUCOAS_bw FUM_fw FUM_bw MDH_fw MDH_bw
          0 10.562660 1.216188 1.404998 0.099471 3.557768 3.074042 3.352469 0.542166 1.232227 1.982324 0.092180 1.478769 2.925053 0.741057 3.328234 3.263405 0.000000
          1 9.670392 0.173741 2.871062 0.360367 0.867981 1.537021 1.725970 0.000000 0.949038 0.000000 0.092180 0.492923 2.925053 1.852641 1.849019 2.331004 4.195807
          2 9.967815 1.389929 3.604094 0.316884 3.557768 2.305531 1.725970 0.542166 2.081794 0.566378 0.153634 2.464616 2.437544 1.852641 1.109411 0.932402 0.932402
          3 11.157505 0.173741 2.504546 0.360367 3.173513 1.537021 4.978967 2.168665 1.798605 2.548702 0.030727 1.971692 0.000000 0.370528 3.328234 2.331004 0.932402
          4 10.860082 0.694965 3.604094 0.447332 0.483726 0.768510 2.810302 0.542166 0.665849 1.699134 0.030727 0.985846 0.000000 2.593698 2.218822 2.331004 4.195807
          ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
          999995 10.860082 0.000000 0.671967 0.490815 3.173513 0.000000 3.352469 4.337330 0.099471 0.849567 0.122907 1.478769 0.487509 0.370528 0.369804 1.864803 4.195807
          999996 8.480702 0.694965 2.138030 0.229919 3.557768 1.921276 1.725970 4.337330 1.798605 2.548702 0.215087 2.957539 2.437544 1.852641 3.328234 1.398602 0.466201
          999997 8.778125 0.521223 3.237578 0.316884 3.557768 1.152766 1.725970 4.337330 1.232227 0.566378 0.000000 1.478769 2.437544 3.334754 1.479215 2.331004 0.000000
          999998 9.967815 0.521223 1.771514 0.403849 1.636492 2.305531 3.352469 1.084333 1.232227 2.548702 0.122907 2.957539 1.462527 2.593698 1.479215 0.932402 3.263405
          999999 10.562660 1.216188 0.671967 0.273401 2.405002 1.921276 3.352469 3.795164 2.364984 0.283189 0.184360 3.450462 2.925053 0.000000 2.218822 3.729606 1.864803

          1000000 rows × 17 columns

            [1]:
            import warnings
            warnings.filterwarnings("ignore")

            import pandas as pd
            import cobra
            import numpy as np
            from cobra.io import load_model
            from cnapy.core import make_scenario_feasible
            openjdk version "23.0.2-internal" 2025-01-21
            OpenJDK Runtime Environment (build 23.0.2-internal-adhoc.conda.src)
            OpenJDK 64-Bit Server VM (build 23.0.2-internal-adhoc.conda.src, mixed mode, sharing)
            
            [2]:
            # Carregar o modelo iJO1366
            model_anaero = load_model('iJO1366')
            model_anaero.reactions.get_by_id('EX_o2_e').bounds = (0,0)

            # As reaçoes que serao tratadas
            R_imp = ['GLCptspp','PDH','PPC','CS','ACONTa','ACONTb','ICDHyr','AKGDH','SUCOAS','FUM','MDH']

            # Carregar todas as 1000000 de combinaçoes geradas na pt_1
            # Esse .to_dict('list') vai fazer um dicionario com as chaves sendo as enzimas e os valores sendo uma lista com os 1000000 de valores das mesmas
            df_combinaçoes = pd.read_csv('combinaçoes.csv')
            combinaçoes = df_combinaçoes.to_dict('list')

            combinations = []

            for i in range(100):
            x = []
            for j in range(11):
            x.append(combinaçoes[R_imp[j]][i])
            combinations.append(x)

            # Inicializa os CSVs com os headers (apenas 1 vez)
            pd.DataFrame(columns = ['Combinaçao','Produçao de NADH','Crescimento']).to_csv('df_final_combinations.csv', index=False)
            pd.DataFrame(columns = R_imp + ['Combinaçao Final']).to_csv('df_final_changes.csv', index=False)

            # Loop principal
            for i in range(len(combinations)):

            # Variavel para ver se precisa entrar ou nao no algoritmo de resolver a inviabilidade
            entrou = False
            try:
            # Vai criar os dicionarios para armazenarem os valores calculados
            dicio_analisado = {'Combinaçao':[],'Produçao de NADH':[],'Crescimento':[]}
            dicio_mudanças = {k: [] for k in R_imp}

            # Esse entra so quando precisa de alteraçao nos valores das enzimas para dar feasible
            Set parameter Username
            Set parameter LicenseID to value 2644080
            Academic license - for non-commercial use only - expires 2026-03-29
            <Solution 0.618 at 0x7fdd0ebe8d30>
            <Solution 1.505 at 0x7fdd0ebd6080>
            <Solution 0.883 at 0x7fdd0ea8c1c0>
            <Solution 0.421 at 0x7fdd0eb7dc30>
            <Solution 1.554 at 0x7fdd0ebff4f0>
            <Solution 5.628 at 0x7fdd0ea51000>
            <Solution 0.348 at 0x7fdd0ea76aa0>
            <Solution 0.088 at 0x7fdd0ea58580>
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            <Solution 0.750 at 0x7fdd0e92b910>
            <Solution 0.618 at 0x7fdd0e981570>
            <Solution 5.401 at 0x7fdd0e963040>
            <Solution 3.273 at 0x7fdd0e8f8a90>
            <Solution 1.279 at 0x7fdd0e7824d0>
            <Solution 0.088 at 0x7fdd0e94dfc0>
            <Solution 1.707 at 0x7fdd0ea52ef0>
            <Solution 2.455 at 0x7fdd0eb0c160>
            <Solution 0.375 at 0x7fdd0ebff1f0>
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            <Solution 0.348 at 0x7fdd0eb6e890>
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            <Solution 1.504 at 0x7fdd0eab30a0>
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            <Solution 0.252 at 0x7fdd0e7c3a60>
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            <Solution 0.706 at 0x7fdd0e6c08b0>
            <Solution 1.909 at 0x7fdd0e649990>
            <Solution 2.870 at 0x7fdd0e6ae7d0>
            <Solution 5.245 at 0x7fdd0e6b3af0>
            <Solution 5.161 at 0x7fdd0e644d00>
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            <Solution 4.053 at 0x7fdd0e4fc310>
            <Solution 3.282 at 0x7fdd0e5f80d0>
            <Solution 0.905 at 0x7fdd0e5fbe20>
            <Solution 0.441 at 0x7fdd0e5d3b50>
            <Solution 4.667 at 0x7fdd0e4cb910>
            <Solution 4.330 at 0x7fdd0e46f7c0>
            <Solution 0.620 at 0x7fdd0e869300>
            <Solution 1.127 at 0x7fdd0ea8e500>
            <Solution 0.177 at 0x7fdd0eb71d20>
            <Solution 1.997 at 0x7fdd0eb0d510>
            <Solution 3.798 at 0x7fdd0ebeaa70>
            <Solution 2.255 at 0x7fdd0ea5b160>
            <Solution 0.441 at 0x7fdd0e99e8f0>
            <Solution 0.618 at 0x7fdd0e4c84f0>
            <Solution 0.355 at 0x7fdd0e7009d0>
            <Solution 3.861 at 0x7fdd0e500f70>
            <Solution 4.160 at 0x7fdd0e661420>
            <Solution 0.441 at 0x7fdd0e1c9990>
            <Solution 2.716 at 0x7fdd0e199de0>
            <Solution 0.845 at 0x7fdd0e6c5f60>
            <Solution 0.333 at 0x7fdd0e9516f0>
            <Solution 0.902 at 0x7fdd0e6b2e30>
            <Solution 0.267 at 0x7fdd0eab9480>
            <Solution 1.150 at 0x7fdd0e7c2500>
            <Solution 2.421 at 0x7fdd0ea81c30>
            <Solution 0.053 at 0x7fdd0eb4e080>
            <Solution 0.776 at 0x7fdd0ebaea40>
            <Solution 2.168 at 0x7fdd0e9c77c0>
            <Solution 0.745 at 0x7fdd0eb7a140>
            <Solution 1.190 at 0x7fdd0e86a3e0>
            <Solution 1.400 at 0x7fdd0e5d15a0>
            <Solution 3.397 at 0x7fdd0e13d720>
            <Solution 1.168 at 0x7fdd0e369fc0>
            <Solution 0.949 at 0x7fdd0df8a1a0>
            <Solution 3.293 at 0x7fdd0e46fb80>
            <Solution 1.504 at 0x7fdd0e8d7250>
            <Solution 0.795 at 0x7fdd0e6c2f20>
            <Solution 1.546 at 0x7fdd0ea8ee00>
            <Solution 0.353 at 0x7fdd0ebfc430>
            <Solution 1.780 at 0x7fdd0eb7f220>
            <Solution 1.721 at 0x7fdd0eb0f190>
            <Solution 3.256 at 0x7fdd0e8f9600>
            <Solution 5.368 at 0x7fdd0ebd7e20>
            <Solution 0.618 at 0x7fdd0ea67970>
            <Solution 0.752 at 0x7fdd0ea76a70>
            <Solution 1.980 at 0x7fdd0e83eb00>
            <Solution 2.699 at 0x7fdd0e8cee00>
            <Solution 1.977 at 0x7fdd0e72e590>
            <Solution 0.593 at 0x7fdd0e3978e0>
            <Solution 0.618 at 0x7fdd0e241ff0>
            <Solution 2.499 at 0x7fdd0e4c8430>
            <Solution 4.468 at 0x7fdd0e052020>
            <Solution 0.318 at 0x7fdd0eb9e380>
            
            [12]:
            import multiprocessing
              [1]:
              import pandas as pd
              import matplotlib.pyplot as plt
              from sklearn.preprocessing import MinMaxScaler, StandardScaler
              [2]:
              df = pd.read_csv('Final_Data.csv')
              [49]:
              df
              [49]:
              GLCptspp_fw PDH_fw PPC_fw CS_fw ACONTa_fw ACONTa_bw ACONTb_fw ACONTb_bw ICDHyr_fw ICDHyr_bw AKGDH_fw SUCOAS_fw SUCOAS_bw FUM_fw FUM_bw MDH_fw MDH_bw Produção de NADH Biomassa
              0 10.562660 1.216188 1.404998 0.483726 3.557768 3.074042 1.025892 0.542166 1.232227 1.232227 0.00000 1.478769 1.478769 0.741057 1.224783 0.000000 0.000000 16.679557 0.000000
              1 9.670392 0.173741 2.871062 0.360367 0.867981 0.507615 0.360367 0.000000 0.360367 0.000000 0.09218 0.492923 0.582421 1.852641 1.849019 2.331004 4.195807 17.698335 0.005113
              2 9.967815 1.389929 3.604094 1.183804 3.489335 2.305531 1.725970 0.542166 1.750182 0.566378 0.00000 2.454197 2.437544 1.131901 1.109411 0.932402 0.932402 19.835517 0.031742
              3 11.157505 0.173741 2.504546 1.636492 3.173513 1.537021 3.805157 2.168665 1.798605 1.798605 0.00000 0.000000 0.000000 0.370528 0.608418 2.331004 0.932402 19.719451 0.000000
              4 10.860082 0.694965 3.604094 0.447332 0.483726 0.036394 0.989498 0.542166 0.665849 0.603791 0.00000 0.030401 0.000000 2.259878 2.218822 2.331004 4.195807 22.306992 0.057947
              ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
              999995 10.860082 0.000000 0.671967 0.000000 0.000000 0.000000 3.352469 3.352469 0.099471 0.099471 0.00000 0.487509 0.487509 0.369804 0.369804 1.864803 2.536770 17.204439 0.000000
              999996 8.480702 0.694965 2.138030 0.229919 2.151195 1.921276 1.725970 1.496051 1.798605 1.798605 0.00000 2.437544 2.437544 1.852641 2.548761 0.000000 0.466201 13.985866 0.000000
              999997 8.778125 0.521223 3.237578 0.316884 1.469650 1.152766 1.725970 1.409086 0.883262 0.566378 0.00000 1.478769 1.445357 1.524338 1.479215 0.045123 0.000000 18.011220 0.063688
              999998 9.967815 0.521223 1.771514 0.000000 1.636492 1.636492 1.084333 1.084333 1.232227 1.232227 0.00000 1.462527 1.462527 1.479215 1.479215 0.932402 2.703916 16.168368 0.000000
              999999 10.562660 1.216188 0.671967 0.273401 2.194677 1.921276 3.352469 3.079067 0.556591 0.283189 0.00000 2.953313 2.925053 0.000000 0.000000 1.864803 1.864803 20.419944 0.053867

              1000000 rows × 19 columns

              [20]:
              df_geral = df.copy()
              df_restrito = df.copy()

              df_restrito = df_restrito.loc[(df['Biomassa']>=0.05)&(df['Produção de NADH'] >=24),]

              df_geral.drop(index=df_restrito.index,inplace=True)
              df_geral['Label'] = ['Geral' for i in range(len(df_geral))]
              [4]:
              GLCptspp_fw PDH_fw PPC_fw CS_fw ACONTa_fw ACONTa_bw ACONTb_fw ACONTb_bw ICDHyr_fw ICDHyr_bw AKGDH_fw SUCOAS_fw SUCOAS_bw FUM_fw FUM_bw MDH_fw MDH_bw Produção de NADH Biomassa Label
              0 10.562660 1.216188 1.404998 0.483726 3.557768 3.074042 1.025892 0.542166 1.232227 1.232227 0.00000 1.478769 1.478769 0.741057 1.224783 0.000000 0.000000 16.679557 0.000000 Geral
              1 9.670392 0.173741 2.871062 0.360367 0.867981 0.507615 0.360367 0.000000 0.360367 0.000000 0.09218 0.492923 0.582421 1.852641 1.849019 2.331004 4.195807 17.698335 0.005113 Geral
              2 9.967815 1.389929 3.604094 1.183804 3.489335 2.305531 1.725970 0.542166 1.750182 0.566378 0.00000 2.454197 2.437544 1.131901 1.109411 0.932402 0.932402 19.835517 0.031742 Geral
              3 11.157505 0.173741 2.504546 1.636492 3.173513 1.537021 3.805157 2.168665 1.798605 1.798605 0.00000 0.000000 0.000000 0.370528 0.608418 2.331004 0.932402 19.719451 0.000000 Geral
              4 10.860082 0.694965 3.604094 0.447332 0.483726 0.036394 0.989498 0.542166 0.665849 0.603791 0.00000 0.030401 0.000000 2.259878 2.218822 2.331004 4.195807 22.306992 0.057947 Geral
              ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
              999995 10.860082 0.000000 0.671967 0.000000 0.000000 0.000000 3.352469 3.352469 0.099471 0.099471 0.00000 0.487509 0.487509 0.369804 0.369804 1.864803 2.536770 17.204439 0.000000 Geral
              999996 8.480702 0.694965 2.138030 0.229919 2.151195 1.921276 1.725970 1.496051 1.798605 1.798605 0.00000 2.437544 2.437544 1.852641 2.548761 0.000000 0.466201 13.985866 0.000000 Geral
              999997 8.778125 0.521223 3.237578 0.316884 1.469650 1.152766 1.725970 1.409086 0.883262 0.566378 0.00000 1.478769 1.445357 1.524338 1.479215 0.045123 0.000000 18.011220 0.063688 Geral
              999998 9.967815 0.521223 1.771514 0.000000 1.636492 1.636492 1.084333 1.084333 1.232227 1.232227 0.00000 1.462527 1.462527 1.479215 1.479215 0.932402 2.703916 16.168368 0.000000 Geral
              999999 10.562660 1.216188 0.671967 0.273401 2.194677 1.921276 3.352469 3.079067 0.556591 0.283189 0.00000 2.953313 2.925053 0.000000 0.000000 1.864803 1.864803 20.419944 0.053867 Geral

              999751 rows × 20 columns

              [5]:
              GLCptspp_fw PDH_fw PPC_fw CS_fw ACONTa_fw ACONTa_bw ACONTb_fw ACONTb_bw ICDHyr_fw ICDHyr_bw AKGDH_fw SUCOAS_fw SUCOAS_bw FUM_fw FUM_bw MDH_fw MDH_bw Produção de NADH Biomassa
              682 10.265237 1.563670 2.871062 0.142954 0.527209 0.384255 1.183804 1.040850 0.426143 0.283189 0.000000 0.519631 0.487509 0.370528 0.369804 4.195807 4.195807 24.417125 0.061228
              1251 10.860082 1.389929 3.604094 0.360367 0.360367 0.000000 2.810302 2.449936 0.643556 0.283189 0.000000 1.971692 1.945435 0.035460 0.000000 4.195807 4.195807 24.657655 0.050049
              11359 11.157505 1.042447 3.237578 0.490815 0.490815 0.000000 2.117313 1.626499 1.340382 0.849567 0.061453 0.492923 0.526793 0.407055 0.369804 4.195807 4.195807 24.592242 0.052577
              11831 10.562660 1.389929 2.871062 0.309687 0.483726 0.174040 0.851853 0.542166 0.592876 0.283189 0.245814 0.000000 0.214524 1.151667 1.109411 3.729606 3.729606 24.214517 0.059641
              13024 10.562660 1.563670 3.604094 0.099471 0.099471 0.000000 1.183804 1.084333 0.949038 0.849567 0.000000 0.518868 0.487509 0.412153 0.369804 3.263405 3.263405 24.057084 0.059772
              ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
              967341 11.157505 1.216188 3.604094 0.447332 0.483726 0.036394 1.531665 1.084333 0.730521 0.283189 0.153634 0.492923 0.614988 0.412436 0.369804 2.797205 3.263405 24.006515 0.060172
              973918 10.562660 1.042447 3.237578 0.192313 0.960823 0.768510 0.192313 0.000000 0.192313 0.000000 0.122907 0.492923 0.581830 0.785524 0.739607 3.729606 4.195807 24.080630 0.064808
              977070 10.265237 1.563670 3.237578 0.099471 1.252237 1.152766 0.099471 0.000000 0.949038 0.849567 0.000000 0.518514 0.487509 0.000000 0.000000 4.195807 4.195807 24.469771 0.059098
              978778 11.157505 1.389929 3.604094 0.338586 0.867981 0.529395 1.965085 1.626499 0.338586 0.000000 0.276540 0.492923 0.739069 0.041047 0.000000 2.838252 2.797205 24.245067 0.057935
              988765 10.265237 1.563670 2.504546 0.316884 0.701139 0.384255 0.316884 0.000000 0.600073 0.283189 0.245814 0.000000 0.213570 0.783151 0.739607 3.773150 3.729606 24.031348 0.061459

              249 rows × 19 columns

              [21]:
              GLCptspp_fw PDH_fw PPC_fw CS_fw ACONTa_fw ACONTa_bw ACONTb_fw ACONTb_bw ICDHyr_fw ICDHyr_bw AKGDH_fw SUCOAS_fw SUCOAS_bw FUM_fw FUM_bw MDH_fw MDH_bw Produção de NADH Biomassa Label
              0 10.562660 1.216188 1.404998 0.483726 3.557768 3.074042 1.025892 0.542166 1.232227 1.232227 0.000000 1.478769 1.478769 0.741057 1.224783 0.000000 0.000000 16.6796 0.000000 Geral
              1 9.670392 0.173741 2.871062 0.360367 0.867981 0.507615 0.360367 0.000000 0.360367 0.000000 0.092180 0.492923 0.582421 1.852641 1.849019 2.331004 4.195807 17.6983 0.005113 Geral
              2 9.967815 1.389929 3.604094 1.183804 3.489335 2.305531 1.725970 0.542166 1.750182 0.566378 0.000000 2.454197 2.437544 1.131901 1.109411 0.932402 0.932402 19.8355 0.031742 Geral
              4 10.860082 0.694965 3.604094 0.447332 0.483726 0.036394 0.989498 0.542166 0.665849 0.603791 0.000000 0.030401 0.000000 2.259878 2.218822 2.331004 4.195807 22.3070 0.057947 Geral
              5 8.480702 1.389929 2.504546 0.316884 0.316884 0.000000 0.316884 0.000000 1.232227 1.132756 0.030727 0.492923 0.492814 1.520858 1.479215 1.398602 1.398602 19.3749 0.058776 Geral
              ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
              999232 11.157505 0.347482 2.138030 0.447332 2.020747 1.573415 2.073831 1.626499 2.081794 1.982324 0.000000 3.428322 3.412562 1.500499 1.479215 2.700149 2.331004 20.4540 0.030040 Geral
              999575 10.860082 0.347482 2.871062 0.099471 0.867981 0.768510 3.352469 3.252998 2.648173 2.548702 0.030727 1.478769 1.488136 0.768453 0.739607 3.758452 3.729606 21.8867 0.040713 Geral
              999724 10.860082 1.216188 2.138030 1.636492 2.789257 1.152766 4.889489 3.252998 1.232227 0.566378 0.000000 1.467821 1.462527 2.223170 2.218822 0.974990 0.000000 19.2799 0.010091 Geral
              999797 9.372970 0.347482 3.237578 0.483726 2.789257 2.305531 1.568059 1.084333 1.232227 1.192257 0.000000 1.482107 1.462527 2.984873 2.958430 3.729606 3.263405 20.9188 0.037322 Geral
              999894 8.778125 0.521223 2.504546 1.252237 2.789257 1.537021 2.336569 1.084333 0.949038 0.936683 0.000000 3.418615 3.412562 1.857193 1.849019 0.466201 0.466201 16.0096 0.011537 Geral

              37142 rows × 20 columns

              [22]:
              GLCptspp_fw PDH_fw PPC_fw CS_fw ACONTa_fw ACONTa_bw ACONTb_fw ACONTb_bw ICDHyr_fw ICDHyr_bw AKGDH_fw SUCOAS_fw SUCOAS_bw FUM_fw FUM_bw MDH_fw MDH_bw Produção de NADH Biomassa Label
              682 10.265237 1.563670 2.871062 0.142954 0.527209 0.384255 1.183804 1.040850 0.426143 0.283189 0.000000 0.519631 0.487509 0.370528 0.369804 4.195807 4.195807 24.42 0.061228 Melhores
              1251 10.860082 1.389929 3.604094 0.360367 0.360367 0.000000 2.810302 2.449936 0.643556 0.283189 0.000000 1.971692 1.945435 0.035460 0.000000 4.195807 4.195807 24.66 0.050049 Melhores
              11359 11.157505 1.042447 3.237578 0.490815 0.490815 0.000000 2.117313 1.626499 1.340382 0.849567 0.061453 0.492923 0.526793 0.407055 0.369804 4.195807 4.195807 24.59 0.052577 Melhores
              11831 10.562660 1.389929 2.871062 0.309687 0.483726 0.174040 0.851853 0.542166 0.592876 0.283189 0.245814 0.000000 0.214524 1.151667 1.109411 3.729606 3.729606 24.21 0.059641 Melhores
              13024 10.562660 1.563670 3.604094 0.099471 0.099471 0.000000 1.183804 1.084333 0.949038 0.849567 0.000000 0.518868 0.487509 0.412153 0.369804 3.263405 3.263405 24.06 0.059772 Melhores
              ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
              875919 10.265237 0.868706 3.237578 0.099471 0.867981 0.768510 0.099471 0.000000 0.099471 0.000000 0.000000 0.037844 0.000000 0.000000 0.000000 4.195807 4.195807 24.38 0.072134 Melhores
              918573 10.860082 1.389929 3.237578 0.447332 0.447332 0.000000 3.352469 2.905137 0.730521 0.283189 0.000000 0.027327 0.000000 0.741057 0.749979 4.186884 4.195807 24.61 0.052087 Melhores
              945138 11.157505 1.042447 3.237578 0.099471 0.099471 0.000000 0.099471 0.000000 0.382660 0.283189 0.000000 1.980634 1.950035 0.370528 0.370528 4.195807 4.195807 24.62 0.058324 Melhores
              958074 11.157505 1.042447 3.604094 0.447332 1.600098 1.152766 0.641637 0.194305 0.730521 0.283189 0.000000 0.031451 0.000000 0.412277 0.369804 3.305879 3.263405 24.25 0.059948 Melhores
              958825 10.562660 1.042447 3.604094 0.192834 0.192834 0.000000 0.735000 0.542166 0.476023 0.283189 0.122907 0.492923 0.581575 0.046261 0.000000 4.195807 4.195807 24.84 0.065294 Melhores

              78 rows × 20 columns

              <ggplot: (640 x 480)>
              
              [24]:
              GLCptspp_fw PDH_fw PPC_fw CS_fw ACONTa_fw ACONTa_bw ACONTb_fw ACONTb_bw ICDHyr_fw ICDHyr_bw AKGDH_fw SUCOAS_fw SUCOAS_bw FUM_fw FUM_bw MDH_fw MDH_bw Produção de NADH Biomassa Label
              0 10.562660 1.216188 1.404998 0.483726 3.557768 3.074042 1.025892 0.542166 1.232227 1.232227 0.000000 1.478769 1.478769 0.741057 1.224783 0.000000 0.000000 16.6796 0.000000 Geral
              1 9.670392 0.173741 2.871062 0.360367 0.867981 0.507615 0.360367 0.000000 0.360367 0.000000 0.092180 0.492923 0.582421 1.852641 1.849019 2.331004 4.195807 17.6983 0.005113 Geral
              2 9.967815 1.389929 3.604094 1.183804 3.489335 2.305531 1.725970 0.542166 1.750182 0.566378 0.000000 2.454197 2.437544 1.131901 1.109411 0.932402 0.932402 19.8355 0.031742 Geral
              4 10.860082 0.694965 3.604094 0.447332 0.483726 0.036394 0.989498 0.542166 0.665849 0.603791 0.000000 0.030401 0.000000 2.259878 2.218822 2.331004 4.195807 22.3070 0.057947 Geral
              5 8.480702 1.389929 2.504546 0.316884 0.316884 0.000000 0.316884 0.000000 1.232227 1.132756 0.030727 0.492923 0.492814 1.520858 1.479215 1.398602 1.398602 19.3749 0.058776 Geral
              ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
              875919 10.265237 0.868706 3.237578 0.099471 0.867981 0.768510 0.099471 0.000000 0.099471 0.000000 0.000000 0.037844 0.000000 0.000000 0.000000 4.195807 4.195807 24.3800 0.072134 Melhores
              918573 10.860082 1.389929 3.237578 0.447332 0.447332 0.000000 3.352469 2.905137 0.730521 0.283189 0.000000 0.027327 0.000000 0.741057 0.749979 4.186884 4.195807 24.6100 0.052087 Melhores
              945138 11.157505 1.042447 3.237578 0.099471 0.099471 0.000000 0.099471 0.000000 0.382660 0.283189 0.000000 1.980634 1.950035 0.370528 0.370528 4.195807 4.195807 24.6200 0.058324 Melhores
              958074 11.157505 1.042447 3.604094 0.447332 1.600098 1.152766 0.641637 0.194305 0.730521 0.283189 0.000000 0.031451 0.000000 0.412277 0.369804 3.305879 3.263405 24.2500 0.059948 Melhores
              958825 10.562660 1.042447 3.604094 0.192834 0.192834 0.000000 0.735000 0.542166 0.476023 0.283189 0.122907 0.492923 0.581575 0.046261 0.000000 4.195807 4.195807 24.8400 0.065294 Melhores

              37220 rows × 20 columns

              Notebook
              Python (escher)
              Kernel status: Idle
                /home/cristian/.conda/envs/escher/bin/python
                
                Set parameter Username
                Set parameter LicenseID to value 2644080
                Academic license - for non-commercial use only - expires 2026-03-29
                
                [6]:
                #model.reactions.get_by_id('PDH').bounds = (0.19,0.19)
                model.reactions.get_by_id('FUM').bounds = (0.27,0.27)
                model.reactions.get_by_id('PPC').bounds = (22,22)
                [7]:
                solution = model.optimize()

                escher_builder = escher.Builder(
                map_name='iJO1366.Central metabolism',
                reaction_scale=[{'type': 'min', 'color': '#0000ff', 'size': 10},
                {'type': 'mean', 'color': '#551a8b', 'size': 20},
                {'type': 'max', 'color': '#ff0000', 'size': 40}],

                hide_secondary_metabolites = True,
                reaction_data = solution.fluxes,

                )
                display(escher_builder)
                Downloading Map from https://escher.github.io/1-0-0/6/maps/Escherichia%20coli/iJO1366.Central%20metabolism.json
                
                [16]:
                solution
                [16]:
                Optimal solution with objective value 0.190
                fluxes reduced_costs
                EX_cm_e 0.000000 0.000000e+00
                EX_cmp_e 0.000000 -5.417533e-01
                EX_co2_e 2.574095 0.000000e+00
                EX_cobalt2_e -0.000005 0.000000e+00
                DM_4crsol_c 0.000042 0.000000e+00
                ... ... ...
                RNDR4 0.000000 -3.833160e-03
                RNDR4b 0.000000 -3.833160e-03
                RNTR1c2 0.004964 0.000000e+00
                RNTR2c2 0.005126 0.000000e+00
                RNTR3c2 0.005126 2.775558e-17

                2583 rows × 2 columns

                [ ]:

                ## PDH

                PDH¶

                Read LP format model from file /tmp/tmptzywuw6q.lp
                Reading time = 0.01 seconds
                : 1805 rows, 5166 columns, 20366 nonzeros
                
                Downloading Map from https://escher.github.io/1-0-0/6/maps/Escherichia%20coli/iJO1366.Central%20metabolism.json
                
                Error displaying widget: model not found
                • Melhores Fluxos Interativo.ipynb
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